4 citations · 5 across the 7 of their papers we have counts for
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Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection
Jiabao Guo, Yadian Wang, Hui Ma +7
Real-world face recognition systems are vulnerable to both physical presentation attacks (PAs) and digital forgery attacks (DFs). We aim to achieve comprehensive protection of biom…
Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning
Ajian Liu, Haocheng Yuan, Xiao Guo +13
PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively. However, isolated training of these…
Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering
Jiabao Guo, Ajian Liu, Yunfeng Diao +5
The challenge of Domain Generalization (DG) in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues. Recently, some CLIP-bas…
FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection
Yongze Li, Ning Li, Ajian Liu +7
Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks. Existing methods struggle to simultaneously detect physical…